Instructions to use xiaotinghe/buffer-embedding-002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaotinghe/buffer-embedding-002 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="xiaotinghe/buffer-embedding-002", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("xiaotinghe/buffer-embedding-002", trust_remote_code=True) model = AutoModel.from_pretrained("xiaotinghe/buffer-embedding-002", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 776256b1ffc8f75f0d6d023b04726b4126368d745fd30327cb8a79e0baede214
- Size of remote file:
- 1.65 GB
- SHA256:
- 2d52d56062dce41743e6a21f04e7e725a82ef7eff0a3edc01e610cc2ddd9619f
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